Approaching public perceptions of datafication through the lens of inequality: a case study in public service media

In the emerging field of critical data studies, there is increasing acknowledgement that the negative effects of datafication are not experienced equally by all. Research on data and discrimination in particular has highlighted how already socially unequal populations are discriminated against in data-driven systems. Elsewhere, there is growing interest in public perceptions of datafication, amongst academic researchers interested in producing ‘bottom up’ understandings of the new roles of data in society and non-academic stakeholders keen to establish positive perceptions of data-driven systems. However, research into public perceptions rarely engages with the issue of inequality which is so central in data and discrimination scholarship. Bringing these two issues together, this paper explores public perceptions of datafication through the lens of inequality, focusing on the relationship between understandings and feelings within these perceptions. The paper draws on empirical focus group research into how audiences perceive the data practices that signing in to access BBC digital services enable. The paper shows how inequalities relating to age, dis/ability, poverty and their intersections played a role in shaping perceptions and that these social inequalities informed understandings of and feelings about data practices in complex and diverse ways. It concludes with reflections on the significance of these findings for future research and for data-related policy

Stability assessment of a tailings storage facility using a non-local constitutive model accounting for anisotropic strain-softening

Recent failures of upstream-raised tailings storage facilities (TSF) raised con-cerns on the future use of these dams. While being cost-effective, they entail higher risks than conventional dams, as stability largely relies on the strength of tailings, which are loose and normally-consolidated materials that may exhibit strain-softening during un-drained loading. Current design practice involves limit equilibrium analyses adopting a fully-softened shear strength; while being conservative, this practice neglects the work input required to start the softening process that leads to progressive failure. This paper describes the calibration and application of the NGI-ADPSoft constitutive model to evaluate the potential of static liquefaction of an upstream-raised TSF and provides an indirect measure of resilience. The constitutive model incorporates undrained shear strength anisotropy and a mesh-independent anisotropic post-peak strain softening. The calibration is performed using laboratory testing, including anisotropically-consolidated triaxial compression tests and direct simple shear tests. The peak and residual undrained shear strengths are validated by statistical interpretation of the available CPTu data. It is shown that this numerical exercise is useful to verify the robustness of the TSF design.Comment: NGI-ADPSoft, Plaxis 2D, Strain-softening, Tailings, Static Liquefactio

A Hundred Thousand Lousy Cats (exploring drawing, AI and creativity)

This paper introduces a practice-led project that uses the Google Quick, Draw! project and dataset to explore the potential differences of algorithmic machine or digitally constructed drawings, and fictional associative hand-drawings. The authors use both digital 20-second sketching (the rule set for the Quick, Draw! Project) and more elaborate drawings and collages to then analyse and speculate about the results of these types of visualisations. At this phase of research it seems obvious to label and move the machine drawing to the reductive, the handdrawn to the more complex and associative realm but we seek to unpack this binary. Artificial intelligence and machine-learning are producing a wealth of creative projects, we select a couple of case studies to speak to particular visual artefacts that derive from algorithmic processing. For instance, the (IBM AI) Watson-composed film trailer for Morgan is considered as a creative artefact and looked at for its apparent allure and effect on a creative process. Through this inquiry we contemplate surprises and mistakes that come naturally when producing hand-made works, exploring then, what it means to draw and to work within classification systems in an algorithm-leaning world

AI in Education: learner choice and fundamental rights

This article examines benefits and risks of Artificial Intelligence (AI) in education in relation to fundamental human rights. The article is based on an EU scoping study [Berendt, B., A. Littlejohn, P. Kern, P. Mitros, X. Shacklock, and M. Blakemore. 2017. Big Data for Monitoring Educational Systems. Luxembourg: Publications Office of the European Union. https://publications.europa.eu/en/publication-detail/-/publication/94cb5fc8-473e-11e7-aea8-01aa75ed71a1/]. The study takes into account the potential for AI and ‘Big Data’ to provide more effective monitoring of the education system in real-time, but also considers the implications for fundamental human rights and freedoms of both teachers and learners. The analysis highlights a need to balance the benefits and risks as AI tools are developed, marketed and deployed. We conclude with a call to embed consideration of the benefits and risks of AI in education as technology tools into the development, marketing and deployment of these tools. There are questions around who – which body or organisation – should take responsibility for regulating AI in education, particularly since AI impacts not only data protection and privacy, but on fundamental rights in general. Given AI’s global impact, it should be regulated at a trans-national level, with a global organisation such as the UN taking on this role